Substation Analysis
Craig Nicholson
Abstract
I will describe the votlage, megawatts, megavars (receieved and delivered), and power factor (lagging and leading) in terms of the electricity. Key goals are to understand substations and meters with regards to voltage and power factor as well as megawatts and megavars and to review the relatioship between power factor and voltage.
Introduction
Electric utilities collect meter readings in time intervals in various units. The intervals can be from 1 minute to 60 minute intervals collecing kW, kWh, VARh, volts. An utility needs to maintain a specified range for voltage across their electric network. When voltage is too low brown outs or electric motors may fail to work and when voltage is too high appliances and equiment can overheat, burn up, and possibly explode.
Power Factor
“A negative power factor (Lagging) occurs when the device (which is normally the load) generates power, which then flows back towards the source, which is normally considered the generator.”[1]
“In an electric power system, a load with a low power factor draws more current than a load with a high power factor for the same amount of useful power transferred. The higher currents increase the energy lost in the distribution system, and require larger wires and other equipment. Because of the costs of larger equipment and wasted energy, electrical utilities will usually charge a higher cost to industrial or commercial customers where there is a low power factor.” [1]
“Power factors below 1.0 require a utility to generate more than the minimum volt-amperes necessary to supply the real power (watts).
This increases generation and transmission costs. For example, if the load power factor were as low as 0.7, the apparent power would be 1.4 times the real power used by the load. Line current in the circuit would also be 1.4 times the current required at 1.0 power factor, so the losses in the circuit would be doubled.” [1] (since they are proportional to the square of the current).
“Alternatively all components of the system such as generators, conductors, transformers, and switchgear would be increased in size (and cost) to carry the extra current.” [1]
“Utilities typically charge additional costs to commercial customers who have a power factor below some limit, which is typically 0.9 to 0.95. Engineers are often interested in the power factor of a load as one of the factors that affect the efficiency of power transmission.” [1]
Definitions
Power factors are usually stated as “leading” or “lagging” to show the sign of the phase angle. Capacitive loads are leading (current leads voltage) and supply power, and inductive loads are lagging (current lags voltage) and consume power.
Univariate Plots Section
Voltage
Range of days: 2016-02-28, 2016-03-09
Summary of Voltage: 100.6, 120.8, 122.4, 122, 123.4, 130
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 100.6 120.8 122.4 122.0 123.4 130.0
Power Factor
MegaWatts, MVARs, and MVARS Squared
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 5.832 7.704 8.962 10.840 25.240
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -3.572 -1.274 -0.754 -0.237 0.752 7.080 240
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.567 1.210 3.040 2.760 50.130 240
Univariate Analysis
What is the structure of your dataset?
Voltage Dataset
The voltage dataset contains endpoints collecting readings every 15 minutes.
## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame': 3031348 obs. of 10 variables:
## $ substationName: chr "HIGHLAND PARK" "DALLAS" "HIGHLAND PARK" "HOUSTON" ...
## $ meter : Factor w/ 2939 levels "200001","200002",..: 637 1979 38 1419 2335 2437 1806 346 2794 615 ...
## $ readdate : Factor w/ 1056 levels "2016-02-28 00:15:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ dtReadDate : POSIXct, format: "2016-02-28" "2016-02-28" ...
## $ dtReadDay : POSIXct, format: "2016-02-28" "2016-02-28" ...
## $ h : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hm : chr "00:15" "00:15" "00:15" "00:15" ...
## $ Voltage : num 226 230 230 230 230 ...
## $ VoltsHalf : num 113 115 115 115 115 ...
## $ voltage.bucket: Factor w/ 4 levels "(0,114]","(114,120]",..: 1 2 2 2 2 2 2 2 2 2 ...Power Factor Dataset
The power factor dataset contains endpoints collecting readings every 60 minutes.
## Classes 'tbl_df', 'tbl' and 'data.frame': 3600 obs. of 16 variables:
## $ substationName: chr "ARLINGTON" "ARLINGTON" "ARLINGTON" "ARLINGTON" ...
## $ ReadDate : Factor w/ 240 levels "2016-02-28 00:10:00.000",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ dtReadDate : POSIXct, format: "2016-02-28 00:10:00" "2016-02-28 01:10:00" ...
## $ dtReadDay : POSIXct, format: "2016-02-28" "2016-02-28" ...
## $ h : num 0 1 2 3 4 5 6 7 8 9 ...
## $ mw : num 8.8 8.75 8.92 9.17 9.55 ...
## $ mvar.delivered: num 0.913 0.92 0.946 1.004 1.067 ...
## $ mvar.received : num 0 0 0 0 0 ...
## $ mvar : num 0.913 0.92 0.946 1.004 1.067 ...
## $ mwsquared : num 77.5 76.5 79.5 84.1 91.3 ...
## $ mvarsquared : num 0.834 0.846 0.895 1.008 1.138 ...
## $ va : num 8.85 8.79 8.97 9.22 9.61 ...
## $ pf : num 0.995 0.995 0.994 0.994 0.994 ...
## $ pfChart : num 0.995 0.995 0.994 0.994 0.994 ...
## $ desc : Factor w/ 2 levels "Lagging","Leading": 1 1 1 1 1 1 2 2 2 2 ...
## $ pf.range : Factor w/ 7 levels "(0,0.88.]","(0.88,0.90]",..: 7 7 7 7 7 7 7 6 6 6 ...Substation Dataset
Used for mapping the Voltage and Power Factor datasets together.
## 'data.frame': 27 obs. of 2 variables:
## $ station : Factor w/ 27 levels "BOYN","CHPH",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ substationName: Factor w/ 27 levels "Arlington","Austin",..: 4 9 5 17 11 24 6 26 10 15 ...Structure of Voltage Data - uncleaned
## 'data.frame': 3033369 obs. of 4 variables:
## $ readdate : Factor w/ 1056 levels "2016-02-28 00:15:00",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Voltage : num 235 237 237 236 236 ...
## $ substationName: Factor w/ 24 levels "Area 51","Arlington",..: 12 12 12 12 12 12 12 12 12 12 ...
## $ meter : int 300063 300063 300063 300063 300063 300063 300063 300063 300063 300063 ...Structure of Power Factor - uncleaned
## 'data.frame': 10800 obs. of 4 variables:
## $ ReadValue: num 0 0 0 0 0 0 0 0 0 0 ...
## $ station : Factor w/ 15 levels "BOYN","CHPH",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ name : Factor w/ 3 levels "PMQD3D","PMQD3R",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ReadDate : Factor w/ 240 levels "2016-02-28 00:10:00.000",..: 1 2 3 4 5 6 235 236 237 238 ...What is/are the main feature(s) of interest in your dataset?
Voltage and Power Factor by time interval for each deliver point (substation) are the main features. One goal is to determine if we can predict power factor.
What other features in the dataset do you think will help support your
investigation into your feature(s) of interest? * mega watt
* mega var delivered
* mega var received
Did you create any new variables from existing variables in the dataset?
I created the power factor, mvars and the direction of the power factor (lagging and leading).
New variables where created for these datasets:
powerfactor_tidy and voltage_tidy
- dtReadDate
- dtReadDay
- h
- hm
voltage_tidy
- voltage.bucket
- VoltsHalf
powerfactor_tidy
- mvars
- mvarsquared
- Power Factor
- Power Factor Chart (Shifted Value)
- desc - the direction of the power factor (Lagging vs Leading)
Of the features you investigated, were there any unusual distributions?
Austin and Dallas have bimodel distibutions of voltage for the days being reviewed.
The long leading tail on the voltage histogram, has a larger range in the data in the lower range than in the upper range.
Number of voltage intervals < 117: 43458
The range is : 100.6, 116.95
Number of voltage intervals > 126: 41439
The range is : 126.05, 129.95
Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?
Various methods were used to clean the data. For instance the ReadDates for the voltage intervals are in ending interval. The interval starts at 3/2/2016 00:15min and ends on 3/3/2016 00:00. To associate the 15 minute intervals with the correct hour and day, we had to roll back each 15 minute interval by 15 minutes.
The power factor data needed to be pivoted to get the data into a tidy format as well. The orignal data has the MegaWatt, MegaVars Delivered and Received in the same column, these values were split out into their own columns.
The substation names can have leading and trailing spaces so this data needed to be trimed.
Bivariate Plots Section
Voltage Plots
Box Plots
Scatter Plots
Dallas Substation Daily Voltage By Hour
The scatter plot shows the data along the time axis for the intervals for the day. The interesting point in this chart, which is similar to the histogram is how the shading changes from dark to grey, which is the points stacking on top of each other.
It takes 5 points on top of each other to make a solid point on this chart.
This demonstrates how the data is spread out over the ranges through the day by hour.
We can see there seems to be one metering point with high voltage which is consistently above the other voltage readings.
## [1] 0.8827947Dallas Substation Meters
If we search all the meters on the Dallas substation, and sort descending by voltage we can obtain a list of meters with the highest voltage.
| meter | dtReadDay | v.min | v.mean | v.median | v.max | v.intervals |
|---|---|---|---|---|---|---|
| 300686 | 2016-03-06 | 126.15 | 127.5969 | 127.450 | 129.15 | 96 |
| 300686 | 2016-02-29 | 116.65 | 124.7021 | 124.675 | 129.00 | 96 |
| 300686 | 2016-03-09 | 112.65 | 120.2797 | 118.900 | 128.90 | 96 |
Voltage Descriptive Statistics
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 100.6 120.8 122.4 122.0 123.4 130.0Standard Deviation
## [1] 2.047819Range
## [1] 100.60 129.95Quantiles
## 0% 25% 50% 75% 100%
## 100.60 120.80 122.35 123.45 129.95Mega Watts
Mega Watts Descriptive Statistics
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 5.832 7.704 8.962 10.840 25.240MegaVAR
MegaVAR Descriptive Statistics
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -3.572 -1.274 -0.754 -0.237 0.752 7.080 240System Total MegaWatts, Mega VARs, MegaVars Squared
